Document once, include anywhere
DocOnce is a modestly tagged (Markdown-like) markup language targeting scientific reports, software documentation, books, blog posts, and slides involving much math and code in the text. From DocOnce source you can generate LaTeX, Sphinx, HTML, IPython notebooks, Markdown, MediaWiki, and other formats. This means that you get the most up-to-date publishing technologies for paper, tablets, and phones.
Highlights
- DocOnce is a modestly tagged markup language (see syntax example), quite like Markdown, but with many more features, aimed at documents with much math and code in the text (see demo).
- There is extensive support for book projects. In addition to classical LaTeX-based paper books one gets for free fully responsive, modern-looking, HTML-based ebooks for tablets and phones. Parts of books can, e.g., appear in blog posts for discussion and as IPython notebooks for experimentation and annotation.
- For documents with math and code, you can generate clean plain LaTeX (PDF), HTML (with MathJax and Pygments - embedded in your own templates), Sphinx for attractive web design, Markdown, IPython notebooks, HTML for Google or Wordpress blog posts, and MediaWiki. The LaTeX output has many fancy layouts for typesetting of computer code.
- DocOnce can also output other formats (though without support for nicely typeset math and code): plain untagged text, Google wiki, Creole wiki, and reStructuredText. From Markdown or reStructuredText you can go to XML, DocBook, epub, OpenOffice/LibreOffice, MS Word, and other formats.
- The document source is first preprocessed by Preprocess and Mako, which gives you full programming capabilities in the document's text. For example, with Mako it is easy to write a book with all computer code examples in two alternative languages (say Matlab and Python), and you can determine the language at compile time of the document. New user-specific features of DocOnce can also be implemented via Mako.
- DocOnce extends Sphinx, Markdown, and MediaWiki output such that LaTeX align environments with labels work for systems of equations. DocOnce also adjusts Sphinx and HTML code such that it is possible to refer to equations outside the current web page.
- DocOnce makes it very easy to write slides with math and code by stripping down running text in a report or book. LaTeX Beamer slides, HTML5 slides (reveal.js, deck.js, dzslides), and Remark (Markdown) slides are supported. Slide elements can be arranged in a grid of cells to easily control the layout.
Demo
A short scientific report demonstrates the many formats that DocOnce can generate and how mathematics and computer code look like. (Note that at the bottom of the page there is a link to another version of the demo with complete DocOnce commands for producing the different versions.)
Another demo shows how DocOnce can be used to create slides in various formats (HTML5 reveal.js, deck.js, etc., as well as LaTeX Beamer).
DocOnce has support for responsive HTML documents with design and functionality based on Bootstrap styles. A demo illustrates the many possibilities for colors and layouts.
DocOnce also has support for exercises in quiz format. Pure quiz files can be automatically uploaded to Kahoot! online quiz games operated through smart phones (with the aid of quiztools for DocOnce to Kahoot! translation).
A complete book (900 pages) has been written entirely in DocOnce. The primary format is a publisher-specific LaTeX style, but HTML or Sphinx formats can easily be generated, such as this chapter in the popular Bootstrap style or the solarized color style as many prefer. Slides can quickly be generated from the raw text in the book. Here are examples in the reveal.js (HTML5) style, or the more traditional LaTeX Beamer style, and even the modern IPython notebook tool, which allows for interactive experimentation and annotation.
Documentation
These documents are under development...
- Tutorial: Sphinx, HTML, PDF
- Manual: Sphinx, HTML, PDF
- Quick Reference: Sphinx, HTML, PDF
- Troubleshooting and FAQ: Sphinx, HTML, PDF
Installation
DocOnce is a pure Python package and installed by
Terminal> sudo python setup.py install
However, DocOnce has a lot of dependencies, depending on what type of formats you want to work with and how advanced constructiosn that are used in the text.
On Debian/Ubuntu it is fairly straightforward to get the packages you need. Basically, you can run a Bash script or an equivalent Python script. Such a script installs a very comprehensive bundle of software. You can read the Installation Guide to get a more detailed description of what is needed of software for various purposes.
If you rely on the Debian package on Linux systems (sudo apt-get install python-doconce
), note that this version of DocOnce is old and out of sync with the documentation.


This tutorial is an introduction to the Ensembl Variation API. Knowledge of the Ensembl Core API and of the concepts and conventions in the Ensembl Core API tutorial is assumed. Documentation about the Variation database schema is available at http://cvsweb.sanger.ac.uk/cgi-bin/cvsweb.cgi/ensembl-variation/schema/ , and while not necessary for this tutorial, an understanding of the database tables may help as many of the adaptor modules are table specific.
Refer to the Ensembl core tutorial for a good description of the coding conventions normally used in Ensembl. Please note that there may be exceptions to these rules in variation.
There are two ways to connect to the EnsEMBL Variation database. The old way uses the Bio::EnsEMBL::Variation::DBSQL::DBAdaptor explicitly. The new one uses the Bio::EnsEMBL::Registry module, which can read either a global or a specific configuration file.
Ensembl variation data, like core data, is stored in a MySQL relational database. If you want to access a variation database, you will need to connect to it. This is done in exactly the same way as to connect to a core database, but using a Variation-specific DBAdaptor.
use Bio::EnsEMBL::Variation::DBSQL::DBAdaptor my $host = 'mysql.ebi.ac.uk'; my $user = 'anonymous'; my $port = 4157; my $dbname = 'oryza_sativa_variation_4_56_6'; my $dbVariation= new Bio::EnsEMBL::Variation::DBSQL::DBAdaptor( -host => $host, -port => $port, -user => $user, -dbname => $dbname);
As for a core connection, in addition to the parameters provided above, the optional port, driver and pass parameters can also be used to specify the TCP connection port, the type of database driver and the password respectively. These values have sensible defaults and can often be omitted.
You will need to have a registry configuration file set up. By default, it takes the file defined by the ENSEMBL_REGISTRY environment variable or the file named .ensembl_init in your home directory if the former is not found. Additionally, it is possible to use a specific file (see perldoc Bio::EnsEMBL::Registry or later in this document for some examples on how to use a different file). An example of such file can be found in ensembl/modules/Bio/EnsEMBL/Utils/ensembl_init.example, and below you have a slightly modified copy of it.
# Example of configuration file used by Bio::EnsEMBL::Registry::load_all # method to store/register all kind of Adaptors. use strict; use Bio::EnsEMBL::Utils::ConfigRegistry; use Bio::EnsEMBL::DBSQL::DBAdaptor; use Bio::EnsEMBL::Variation::DBSQL::DBAdaptor; my @aliases; new Bio::EnsEMBL::DBSQL::DBAdaptor( -host => 'mysql.ebi.ac.uk', -user => 'anonymous', -port => 4157, -species => 'oryza_sativa', -group => 'core', -dbname => 'oryza_sativa_core_4_56_6'); @aliases = ('Oryza sativa japonica', 'Oryza sativa'); Bio::EnsEMBL::Utils::ConfigRegistry->add_alias( -species => 'oryza_sativa', -alias => \@aliases); new Bio::EnsEMBL::Variation::DBSQL::DBAdaptor( -host => 'mysql.ebi.ac.uk', -user => 'anonymous', -port => 4157, -species => 'oryza_sativa', -dbname => 'oryza_sativa_variation_4_56_6'); 1;
In this configuration file, you can list all the parameters needed to connect a variation database. The variation database is a database that contains information about one particular species. However, in order to have full functionality, you will need to connect to the core database as well. The use of the registry configuration file lets you the freedom to list connection parameters for all Ensembl core databases you might need to access in relation to Ensembl variation data (in our example, only 1 is mentioned, human). All this information is then stored in a single central place, easy to maintain (modify and update). The access to a database adaptor is done using either the main species alias (specified by the -species parameter) or one of the aliases specified (in the @aliases array). No need to remember the complete database name, one of the aliases will be enough.
Another way to use the registry without having to use any configuration file, but only if you want to use the latest databases and do not remember names, is to use the following method: Bio::EnsEMBL::Registry->load_registry_from_db( -host => 'mysql.ebi.ac.uk', -user => 'anonymous', -port => 4157 ); This way, you will load the correct versions of the ensembl databases for the software release it can find on a database instance into the registry and also adds a set of standard aliases.
Below is a non exhaustive list of Ensembl variation adaptors that are most often used
Only some of these adaptors will be used for illustration as part of this tutorial through commented perl scripts code.
One of the most important uses for the variation database is to be able to get all variations in a certain region in the genome. Below it is a simple commented perl script to illustrate how to get all variations in chromosome 25 in zebrafish
use strict; use warnings; use Bio::EnsEMBL::Variation::DBSQL::DBAdaptor; use Bio::EnsEMBL::DBSQL::DBAdaptor; # connect to Variation database my $dbVariation = Bio::EnsEMBL::Variation::DBSQL::DBAdaptor->new (-host => 'mysql.ebi.ac.uk', -dbname => 'oryza_sativa_variation_3_55_6', -species => 'japonica', -group => 'variation', -user => 'anonymous'); # connect to Core database my $dbCore = Bio::EnsEMBL::DBSQL::DBAdaptor->new (-host => 'mysq.ebi.ac.uk', -dbname => 'oryza_sativa_core_3_55_6', -species => 'japonica', -group => 'core', -user => 'anonymous'); my $slice_adaptor = $dbCore->get_SliceAdaptor(); #get the database adaptor for Slice objects my $slice = $slice_adaptor->fetch_by_region('chromosome',1); #get chromosome 1 in rice my $vf_adaptor = $dbVariation->get_VariationFeatureAdaptor(); #get adaptor to VariationFeature object my $vfs = $vf_adaptor->fetch_all_by_Slice($slice); #return ALL variations defined in $slice foreach my $vf (@{$vfs}){ print "Variation: ", $vf->variation_name, " with alleles ", $vf->allele_string, " in chromosome ", $slice->seq_region_name, " and position ", $vf->start,"-",$vf->end,"\n"; } exit 0;
Another common use of the variation database is to get the effects that variations make in a transcript. In the example below, it is explained how to get all variations in a particualr chicken transcript and see which is the effect of that variation in the transcript
use strict; use warnings; use Bio::EnsEMBL::Variation::DBSQL::DBAdaptor; use Bio::EnsEMBL::DBSQL::DBAdaptor; # connect to Variation database my $dbVariation = Bio::EnsEMBL::Variation::DBSQL::DBAdaptor->new (-host => 'mysql.ebi.ac.uk', -dbname => 'oryza_sativa_variation_3_55_6', -species => 'japonica', -group => 'variation', -user => 'anonymous'); # connect to Core database my $dbCore = Bio::EnsEMBL::DBSQL::DBAdaptor->new (-host => 'mysq.ebi.ac.uk', -dbname => 'oryza_sativa_core_3_55_6', -species => 'japonica', -group => 'core', -user => 'anonymous'); my $stable_id = 'LOC_Os01g09760.1'; #this is the stable_id of a rice transcript my $transcript_adaptor = $dbCore->get_TranscriptAdaptor(); #get the adaptor to get the Transcript from the database my $transcript = $transcript_adaptor->fetch_by_stable_id($stable_id); #get the Transcript object my $trv_adaptor = $dbVariation->get_TranscriptVariationAdaptor; #get the adaptor to get TranscriptVariation objects my $trvs = $trv_adaptor->fetch_all_by_Transcripts([$transcript]); #get ALL effects of Variations in the Transcript foreach my $tv (@{$trvs}){ print "SNP :",$tv->variation_feature->variation_name, " has a consequence/s ", join(",",@{$tv->consequence_type}), " in transcript ", $stable_id, "\n"; #print the name of the variation and the effect (consequence_type) of the variation in the Transcript } exit 0;
Below is a complete example on how to use the variation API to retrieve different data from the database. In that particular example, we want to get, for a list of variation names, information about alleles, flanking sequences, locations, effects of variations in transcripts, position in the transcript (in case it has a coding effect) and genes containing the transcripts.
use strict; use warnings; use Bio::EnsEMBL::Variation::DBSQL::DBAdaptor; use Bio::EnsEMBL::DBSQL::DBAdaptor; # connect to Variation database my $dbVariation = Bio::EnsEMBL::Variation::DBSQL::DBAdaptor->new (-host => 'mysql.ebi.ac.uk', -dbname => 'oryza_sativa_variation_3_55_6', -species => 'japonica', -group => 'variation', -user => 'anonymous'); # connect to Core database my $dbCore = Bio::EnsEMBL::DBSQL::DBAdaptor->new (-host => 'mysq.ebi.ac.uk', -dbname => 'oryza_sativa_core_3_55_6', -species => 'japonica', -group => 'core', -user => 'anonymous'); my $va_adaptor = $dbVar->get_VariationAdaptor; #get the different adaptors for the different objects needed my $vf_adaptor = $dbVar->get_VariationFeatureAdaptor; my $gene_adaptor = $dbCore->get_GeneAdaptor; my @rsIds = qw(rs17876208 rs17876209); while (@rsIds){ # get Variation object my $var = $va_adaptor->fetch_by_name($_); #get the Variation from the database using the name &get_VariationFeatures($var); } sub get_VariationFeatures{ my $var = shift; # get all VariationFeature objects: might be more than 1 !!! foreach my $vf (@{$vf_adaptor->fetch_all_by_Variation($var)}){ print $vf->variation_name(),","; # print rsID print $vf->allele_string(),","; # print alleles print join(",",@{$vf->get_consequence_type()}),","; # print consequenceType print substr($var->five_prime_flanking_seq,-10) , "[",$vf->allele_string,"]"; #print the allele string print substr($var->three_prime_flanking_seq,0,10), ","; # print RefSeq print $vf->seq_region_name, ":", $vf->start,"-",$vf->end; # print position in Ref in format Chr:start-end &get_TranscriptVariations($vf); # get Transcript information } } sub get_TranscriptVariations{ my $vf = shift; # get all TranscriptVariation objects: might be more than 1 !!! my $transcript_variations = $vf->get_all_TranscriptVariations; #get ALL the effects of the variation in # different Transcripts if (defined $transcript_variations){ foreach my $tv (@{$transcript_variations}){ print ",", $tv->pep_allele_string if (defined $tv->pep_allele_string); # the AA change, but only if it is in a coding region my $gene = $gene_adaptor->fetch_by_transcript_id($tv->transcript->dbID); print ",",$gene->stable_id if (defined $gene->external_name); # and the external gene name } } print "\n"; } exit 0;
In order to be able to use the LD calculation, you need to compile the C source code and install a module, called IPC::Run. There is more information on how to do this in Use LD calculation In the example below, it calculates the LD in a region in human chromosome 6 for a HAPMAP population, but only prints when there is a high LD
use Bio::EnsEMBL::Variation::DBSQL::DBAdaptor; use Bio::EnsEMBL::DBSQL::DBAdaptor; # connect to Variation database my $dbVariation = Bio::EnsEMBL::Variation::DBSQL::DBAdaptor->new (-host => 'ensembldb.ensembl.org', -dbname => 'homo_sapiens_variation_37_35j', -species => 'human', -group => 'variation', -user => 'anonymous'); # connect to Core database my $dbCore = Bio::EnsEMBL::DBSQL::DBAdaptor->new (-host => 'ensembldb.ensembl.org', -dbname => 'homo_sapiens_core_37_35j', -species => 'human', -group => 'core', -user => 'anonymous'); my $chr = 6; #defining the region in chromosome 6 my $start = 25_834_000; my $end = 25_854_000; my $population_name = 'CSHL-HAPMAP:HapMap-CEU'; #we only want LD in this population my $slice_adaptor = $dbCore->get_SliceAdaptor(); #get adaptor for Slice object my $slice = $slice_adaptor->fetch_by_region('chromosome',$chr,$start,$end); #get slice of the region my $population_adaptor = $dbVariation->get_PopulationAdaptor; #get adaptor for Population object my $population = $population_adaptor->fetch_by_name($population_name); #get population object from database my $ldFeatureContainerAdaptor = $dbVariation->get_LDFeatureContainerAdaptor; #get adaptor for LDFeatureContainer object my $ldFeatureContainer = $ldFeatureContainerAdaptor->fetch_by_Slice($slice,$population); #retrieve all LD values in the region foreach my $r_square (@{$ldFeatureContainer->get_all_r_square_values}){ if ($r_square->{r2} > 0.8){ #only print high LD, where high is defined as r2 > 0.8 print "High LD between variations ", $r_square->{variation1}->variation_name,"-", $r_square->{variation2}->variation_name, "\n"; } } exit 0;
With the apparition of the new technologies, one of the new functionalities that the variation API has is the possibility to work with your specific strain as if it was the reference one, and compare it against others. In the example, we create a StrainSlice object for a mouse exon and compare it against the reference exon.
use Bio::EnsEMBL::Variation::DBSQL::DBAdaptor; use Bio::EnsEMBL::DBSQL::DBAdaptor; # connect to Variation database my $dbVariation = Bio::EnsEMBL::Variation::DBSQL::DBAdaptor->new (-host => 'ensembldb.ensembl.org', -dbname => 'mus_musculus_variation_37_34e', -species => 'mouse', -group => 'variation', -user => 'anonymous'); # connect to Core database my $dbCore = Bio::EnsEMBL::DBSQL::DBAdaptor->new (-host => 'ensembldb.ensembl.org', -dbname => 'mus_musculus_core_37_34e', -species => 'mouse', -group => 'core', -user => 'anonymous'); my $exon_stable_id = 'ENSMUSE00000554526'; #mouse exon stable_id my $strain_name = "129X1/SvJ"; #mouse strain name my $exon_adaptor = $dbCore->get_ExonAdaptor; #get adaptor for Exon objects my $exon = $exon_adaptor->fetch_by_stable_id($exon_stable_id); #get exon object print "Reference sequence: ", substr($exon->seq->seq,0,5), "...", substr($exon->seq->seq,82,5),"...", substr($exon->seq->seq,90,5),"...", "\n"; #print exon sequence my $strainSlice = $exon->feature_Slice->get_by_strain($strain_name); #get strainSlice for the exon #print the strain sequence for that exon print "Strain sequence: ", substr($strainSlice->seq,0,5), "...", substr($strainSlice->seq,82,5),"...", substr($strainSlice->seq,90,5),"...", "\n"; my $afs = $strainSlice->get_all_AlleleFeatures_Slice(); #get AlleleFeature between reference and strain sequence in the exon foreach my $af (@{$afs}){ print "Allele Feature start-end-allele_string: ",$af->start,"-",$af->end,"-",$af->allele_string,"\n"; } exit 0;
For additional information or help mail the ensembl-dev mailing list. You will need to subscribe to this mailing list to use it. More information on subscribing to any Ensembl mailing list is available from the Ensembl Genomes Contacts page.


mousetweaks(1) mousetweaks(1) NAME mousetweaks - Accessibility enhancements for pointing devices SYNOPSIS mousetweaks [--dwell] [--ssc] [--dwell-time=FLOAT] [--ssc-time=FLOAT] [-m|--dwell-mode=STRING] [-c|--show-ctw] [-t|--threshold=INT] [-g|--geom‐ etry] [-s|--shutdown] [--daemonize] [-?|--help] DESCRIPTION mousetweaks is a collection of accessibility enhancements for pointing devices. This manual page describes the mousetweaks daemon. OPTIONS --dwell Automatically perform mouse clicks without using a physical but‐ ton. --ssc Trigger the secondary mouse button when the primary mouse button is held down for a specified amount of time. --dwell-time=FLOAT Time to keep the pointer motionless before a dwell click is per‐ formed. Range: 0.2 - 3.0 seconds. --ssc-time=FLOAT Time to hold the primary mouse button before a simulated secondary click is performed. Range: 0.5 - 3.0 seconds. -m, --dwell-mode=STRING Select a dwell mode. Available options: window - Select the click type from a window or a panel applet. gesture - Perform different clicks by moving in different directions. -c, --show-ctw Show the click-type window. Only useful in combination with --dwell-mode=window. -t, --threshold=INT Ignore small pointer movements. Range: 0 - 30 pixels. -g, --geometry Size and position of the click-type window. Expects a standard X Window System geometry string. ie. WIDTHxHEIGHT+X+Y -s, --shutdown Stop the mousetweaks daemon. --daemonize Start mousetweaks in the background, ie. spawn a child process. --login Start mousetweaks in login mode. Login mode enables workarounds specific to GDM. Currently unused. FILES ~/.mousetweaks.pid The daemon creates a lock file in the users home directory to keep track of its status. BUGS Report bugs to <http://bugzilla.gnome.org/> ⟨http://bugzilla.gnome.org/⟩ AUTHORS mousetweaks was written by Gerd Kohlberger. This manual page was written by Francesco Fumanti. LICENSE Licenced under the GNU General Public License v3 or later. SEE ALSO dwell-click-applet(1), pointer-capture-applet(1) mousetweaks(1)


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Technology sometimes seems like magic, especially when we don’t have any idea about how it was done, or we even think it can’t be done at all. When I was a kid, I was a huge fan of Sci-Fi Films, which were on every TV channel in the 1990s in my country. They did inspire me a lot, help grow up an “engineering mindset” in me. Being an engineer sometimes makes me feel so great, because I can mostly understand many things that seem to be magical to others.  Just let me tell you one of them.
It was sometime in 2015, I watched a demo video from a startup company in Japan. It was about Object Recognition using Deep Learning. At that time, I didn’t even have any idea about what Machine Learning is, not to mention Deep Learning! My thought then? “That was impossible. How did they do that?” I kept questioning myself for a while. I just couldn’t get it out of my mind. But unfortunately, because my first thought was “That was impossible”, or at least “That was impossible to me”, I gave it up, left the mystery unrevealed.
It was not until I paid serious attention to Machine Learning that I somehow made the mystery become clear. As I was working around with Convolutional Neural Network, then I suddenly thought of the mystery I gave up one year ago. I mean, that was really exciting and crazy. (Feels like Mission 6 accomplished to me, lol)
Seems like a pretty long beginning (I didn’t mean to write a preface for my book or something, though). So I am here to share my work. In fact, it was not something impresssive (especially when comparing to outstanding projects out there). But I really hope this post can help, especially for ones who have enthusiasm in Machine Learning and once came accross something like me before.
In this project, I will try to reproduce the result I saw in the demo video one year ago. Concretely, I will move the camera around a table, with many objects put on it. The computer will try to recognize each object, and print the object’s label in the output of the camera.
Writing it all in one post may hurt, so I separate this project into two parts like below:
Before I begin, let’s talk a little bit about how Machine Learning works. I had post about what Machine Learning exactly is, and how it exactly works here: What is Machine Learning?
The main part of any Machine Learning system, is the Model. It contains the algorithms and all the associated parameters. A good Model will produce a good prediction. Because of that, the training process (i.e. the process which produces the Model) can be heavily computational and take a lot of resources (you know, RAM and memories to store the parameters).
Fortunately, a great work from François Chollet helps make this problem become much more relaxing than ever.  Sounds cool, right? Among those, I will use the VGG16 Model for this project.
So, let’s get down to business.
Firstly, I downloaded some images for testing purpose, and put them in the same folder.
As written in the code above, I passed the folder name, then got the list of file names stored in files variable. I shuffled the list for demonstration purpose (you don’t want the same order everytime, right?)
Next, I initiate the VGG16 Model, provided by François Chollet. You will have to clone his repository first, from here
As you can see we are passing the weights=”imagenet” parameter, to tell the Model to initialize with pretrained parameter set. It is quite a large file (approximately 550MB), so it will take some time to download on its first run. (The parameter set is saved somewhere in keras’ temporary folder)
Then, I looped through the file list created above, in each iteration:
Keras provides us a method for loading image for training and testing purpose. Note that OpenCV and Keras treat the input image in different ways, so we cannot use image loaded by OpenCV’s imread method for Keras. Concretely, let’s say we have a 3-channel image (a common color image). OpenCV’s imread will produce an (width, height, 3) array, whereas Keras requires an (3, width, height) format. Another way to solve this is to use Numpy’s tranpose method:
I prefer the second approach, you will understand why when we come to the second part of this project.
Next, we need to add one more dimension to the array obtained above. Why we have to do that? If you have experience with Neural Network, you may find the term mini_batch similar. The additional dimension will tell the Model the number of input arrays (for example, you have 70,000 data, so you need to pass an array with shape (70000, depth, width, height) for the Model to run on, let’s say SGD or RMSprop or something).
If you don’t have any idea about what I’ve just talked, you can ignore it for now. I’ll talk about Neural Network in later posts, I promise.
After converting the input image to Keras’s format, I passed it through a pre-processing method:
The pre-processing method came along with the models provided by François Chollet. It simply subtracts each channel with its mean value, and solve the ordering conflict between Theano and Tensorflow.
So we now have things ready. Let’s pass the preprocessed array to the Model and get it predicted:
Let’s talk a little bit about ImageNet’s image database (the database on which VGG16 was trained). It was organized according to WordNet hierarchy (you can find more details here). But the predicting result is always numerical (Neural Network only works with numerical labels), so we have to map between the numerical result, and the noun provided by WordNet.
Once again, François Chollet provided a method to do that: decode_predictions method. It simply map the predicted result with a JSON file, and return the associated noun instead. Here’s what the JSON file looks like:
Finally, the easiest part: load the input image with OpenCV’s imread, then put the label found above on it, then show the result. And we are DONE!
Here are some results I got from my test images:
So I’ve just walked through the first part of the Real time Object Recognition project. You may think that it’s not a big deal anyway. But in my opinion, it did help a lot in visualizing some real work on Object Recognition using Convolutional Neural Network. Just implementing the code to use the model may not take you some months to work on, but importantly, it now doesn’t seem like magic anymore.
That’s all for part 1. Hope you enjoy it. In part 2, I will continue with real time object recognition through continuous input from camera. So stay updated and I’ll see you there.
You can find the second part of this post here: Real Time Object Recognition (Part 2).


The table below contains the information used to predict HLA-I epitopes, the analysis was performed using netMHCpan 4.1. Only epitopes with a 'strong binding' affinity have been selected for this analysis and are included in the table below.
For a list of the HLA-I alleles used click the link
| Gene | Protein CDS Position | HLA-I Allele | Epitope Sequence | Binding Affinity (nM) |
|---|---|---|---|---|
| Gene |


Kolkata (or Calcutta) holds many festivals throughout the year.Durga Puja is the largest festival of West Bengal, and it features colourful pandals, decorative idols of Hindu goddess Durga and her family, lighting decorations and fireworks. Other major festivals are Kali Puja, Holi, Diwali, etc.
The greatest festival of the Bengali community.
Durga Puja ranks among the popular festivals in India.
It is widely celebrated in West Bengal
and other partof eastern India. It is a four day festival
and is dedicated to the worship of Goddess Durga.
Immense fanfare and celebration takes place during this
Durga Puja celebrations.
DID YOU KNOW ?
Huge celebrations and fanfare
take place all over West Bengal
and other parts of eastern India.
People of all age groups wear new
clothes and go for pandal hopping.
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PromiseKit
Promises could be your solution to modern development’s asynchronous woe.
Installing PromiseKit
Drag and drop PromiseKit.xcodeproj into your project;
Import PromiseKit.


The forecast – a significant upwards revision of the IEA’s renewables projections last year – is largely driven by increasing expansion of solar energy in China and India, says the Paris-based organisation.
The global dynamics of renewables expansion will also shift over this five-year period, with capacity growth in India overtaking that of the EU for the first time.
Renewables accounted for almost two-thirds of net capacity additions in 2016, with almost 165 gigawatts (GW) coming online, the IEA adds. A further 920GW of renewable capacity will be installed by 2022, according to its forecasts.
Carbon Brief breaks down the IEA’s numbers for 2016 and the main elements of its renewables forecasts for the next five years.
Solar king
The revised IEA forecast comes after additions of solar PV capacity last year topped those of any other fuel, surpassing coal for the first time.
As the graph below shows, newly added capacity reached 74GW in 2016 – up almost 50 per cent from the amount added a year earlier. In total, renewables provided a record two-thirds of global net capacity additions.
The sector has also seen record-low auction prices of $30 megawatts per hour (MWh), the IEA notes.
Over the next five years, the IEA expects solar to represent by far the largest yearly capacity additions for renewables – well above wind and hydro.
Worldwide, solar PV capacity will hit a total of 740GW by 2022, more than the combined total power capacities of India and Japan today, it says, driven by continuous technology cost reductions and unprecedented market dynamics in China due to policy changes.
It’s worth noting that this new projection for solar PV revises forecasts up by over a third compared to the IEA’s forecasts last year (see below for more on this).
“Undisputed leader”
China accounted for almost half of the solar PV expansion in 2016, the IEA says. As the chart below shows, this was a large increase on its annual additions in 2015.
Chinese companies now account for 60 per cent of the world’s solar cell manufacturing capacity. This means market and policy developments in China will have global implications on the deployment and prices of solar around the world, the IEA says.
China has already surpassed its 2020 solar PV target. The IEA expects it to exceed its wind target in 2019. (It’s also worth noting that the IEA said in 2015 that China would likely be two years late in meeting its wind energy goal.)
Writing in the foreward of the report, Dr Fatih Birol, executive director of the IEA, says China has increased its solar PV capacity almost 800-fold over the past decade and is now the “undisputed leader” in several renewables sectors. He writes:
“
Along with new policies that spur competition in several other countries, this Chinese dynamic has led to record-low announced prices of solar PV and onshore wind, which are now comparable or even lower than new-built fossil fuel alternatives.
Dr Fatih Birol, executive director, IEA
Accelerated forecast
It is important to note that the IEA’s yearly five-year-forecasts have consistently underestimated the growth of renewable energy. The graph tweeted below shows how solar PV forecasts have been revised upwards each year.
“When the IEA and EIA [the US Energy Information Administration] say – as they did for many years after I founded New Energy Finance – that modern renewable energy will produce less than 1 per cent of the world’s electricity many decades into the future, that deters serious politicians, investors or business people from backing them.”
Last year, Carbon Brief took an in-depth look at how the IEA’s forecasts had have been revised upwards over the past four years. Carbon Brief has updated the chart below, which compares IEA forecasts versus actual growth, to include 2017.
IEA forecasts of annual renewable energy capacity additions versus actual growth. Source: Carbon Brief analysis of IEA Medium Term Renewable Market Reports 2013-2017. Chart by Carbon Brief using Highcharts.
Since 2015, the IEA has tried to address these shortcomings by adding an “accelerated” deployment path to its forecasts, whereby governments address policy challenges and lift barriers to deployment.
In this year’s forecast, total renewable capacity growth increases by an extra 30 per cent under this scenario, totalling an added 1,150GW by 2022.
In particular, if challenges in China around the cost of renewable subsidies and grid integration are addressed, its solar PV could reach a total of 320GW by 2022, equivalent to the total capacity of all power in Japan.
If these occurred along with possible policy and regulatory improvements in other key countries, such India, Japan and the US, world solar PV cumulative capacity could almost triple to 880GW by 2022 under this accelerated scenario.
China is in the process of a policy transition in this direction, the IEA says; for instance, through its move away from feed-in-tariffs (FiTs), although the timing and implementation of this remains uncertain.
EU overtaken
While China and the US remains the world’s first- and second-fastest growing renewables markets, respectively, over the forecasted (main case scenario) period, growth in India surpasses the EU for the first time, as the chart below shows.
By 2022, the IEA expects India to have more than doubled its current renewable electricity capacity, with 90 per cent of this from wind and solar.
This will be driven by moves in India to address the financial health of its utilities and tackle grid-integration issues, the IEA says.
Meanwhile, in the IEA’s “accelerated case” – where India’s grid integration and infrastructure challenges are addressed, and policy and regulatory uncertainties reduced – renewable capacity expansion is increased by another third above this level. This would make India the joint second-largest growth market with US after China.
In the main-case forecast, the EU sees its renewables growth fall by 40 per cent between 2017 and 2022 compared with the previous five-year period, due to weaker electricity demand and overcapacity.
Limited visibility on the amount of capacity in upcoming auctions in some markets present a forecast uncertainty, the IEA adds. The new EU Renewable Energy Directivefor the post-2020 period, if adopted, would address this by providing market predictability for investors. (In the UK, the government has yet to set out plans for renewables auctions, following the most recent auction last month where offshore wind emerged as the biggest winner.)
The US remains in second place despite the current political uncertainty, the IEA says. This is because the main drivers for new onshore wind and solar capacities, including state level policies for distributed solar PV and federal tax incentives, remain strong
However, it notes that the current uncertainty over proposed federal tax reforms, international trade, and energy policies could have implications for the relative economics of renewables and their expansion.
Windfall
While wind energy continued to expand in 2016, its annual growth in capacity declined by almost a fifth compared to 2015 (a year which saw a developer rush in China).
Hydropower capacity expansion was also lower than in 2015. The Chinese market declined for a third year in a row, although there was strong growth in Brazil. Despite this, hydropower will remain the largest source of renewable electricity generation in 2022 under the IEA’s forecast, followed by wind, solar PV and bioenergy.
Coal will remain the largest source of electricity generation, however growth in renewable generation will be twice as large as that of gas and coal combined over the next five years.
This will help renewables will close in on coal’s lead, the IEA says in 2016, renewable generation was 34% less than coal, but by 2022 this will be halved to just 17 per cent.
Overall, the share of renewables in power generation will reach 30 per cent in 2022, up from 24 per cent in 2016, reaching over 8,000TWh.
Falling costs
A changing policy environment will support more cost-effective deployment of wind and solar, the IEA says.
Global average generation costs fall by a quarter for utility-scale solar PV, almost 15 per cent for onshore wind, and by a third for offshore wind by 2022, as competition drives down costs.
The most significant policy development in recent years has been the shift from government-set Feed-in-Tariffs (FiTs) to competitive auctions with long-term Power Purchase Agreements (PPAs), the IEA adds. This is set to continue.
Almost half of renewable electricity capacity expansion will come from competitive auctions with PPAs over the next five years. This compares to just over 20 per cent in 2016.
Average levelised costs of energy (LCOEs) for wind and solar are expected to continue decreasing, but to remain higher than auction prices, as the chart below shows.
System integration
The IEA forecasts show wind and solar together representing more than 80 per cent of global renewable capacity growth in the next five years.
Denmark will likely be the world leader in variable renewables in 2022 with a 70 per cent share, largely from wind, as the chart below shows.
The IEA expects the share of wind and solar in total generation to surpass 25 per cent in a number of other European countries, including the UK and Germany. (The UK currently gets 25 per cent of its power from all renewables, including hydro and bioenergy).
In China, India and Brazil, the share of variable generation is expected to double to more than 10 per cent by 2022.
These higher proportions of renewables in electricity generation will make integration of these variable renewables into electricity system critical, the IEA says.
This means policymakers will need to put in place ways to increase system flexibility – such as grid interconnections, electricity storage and demand-side response.
Market and policy frameworks will also need to put other measures in place, such as long-term price signals to attract investment, pricing negative externalities, and fostering a portfolio of dispatchable renewable technologies, such as hydropower and bioenergy.
As the IEA notes, variable renewables are more exposed by losing system value as their proportion in the system increases. This is because their wholesale prices will fall exactly at the moments when their production is high and demand is low.
(An article by Michael Liebrich of Bloomberg New Energy Finance (BNEF) earlier this year looked at the policy options for integrating higher amounts of renewables into electricity networks in a cost competitive way.)
Off-grid
For the first time, the IEA’s forecasts have taken a closer look at how off-grid solar PV applications are developing in Asia and sub-Saharan Africa. This includes industrial applications, solar home systems and mini-grids driven by government electrification programmes.
The IEA finds that off-grid capacity in these regions will almost triple by 2022 to more than 3,000 MW. In particular, solar home systems will bring basic electricity services to 65 million additional people in Asia and sub-Saharan Africa, the IEA says.
Slow moving
As well as penetration of renewables into the electricity grid, the IEA’s forecasts also look at the future of renewables in heat and transport.
Decarbonisation of transport remains a major policy challenge, it says, while the next chapter in the rise of renewables will also require more work on multiplying their applications in buildings and the industrial sector.
In its scenario, the share of renewables in road transport (including biofuels and renewable electricity for electric vehicles) is expected to increase only marginally, from 4.2 per cent in 2016 to 4.8 per cent in 2022.
(It’s worth noting that renewables only account for 26 per cent of global electricity consumption by EVs today; the IEA expects this to increase to 30 per cent by 2022.)
Biofuel production increases modestly by 16 per cent by 2022, with most growth in Asia and Brazil. Advanced biofuel production remains low, at just over 1 per cent of total biofuel production.
(In the IEA’s accelerated scenario – which sees added policy focus and investment put on biofuels in India, Brazil and the US – biofuel production is 16 per cent higher again. However, renewables share of road transport fuel demand still only reaches just over 5 per cent by 2022.)
Heat
Bioenergy will also lead in renewable heat consumption growth, the IEA says. It’s worth noting that almost two fifths of energy-related CO2 emissions come from providing heating and hot water to buildings and industrial processes.
But the IEA expects the overall share of renewables in heat consumption to increase only slowly, rising from 9 per cent in 2015 to 11 per cent in 2022. The EU’s binding Renewable Energy Directive targets will make it the second largest growth market after China, as well as remaining the global leader in terms of absolute renewable heat consumption.
This story was published with permission from Carbon Brief.


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The Arctic (66.6–90°N) is the region experiencing the most rapid increase in surface temperature in response to global warming 1– 4; it is projected to warm by about three times the global average in future greenhouse gas emission scenarios 4. The amplified climate response in the Arctic makes this region particularly susceptible to tipping points in both physical and ecological systems in the near future, with loss of summer sea ice often argued to be the most imminent 5, 6. Arctic warming and sea-ice decline is progressing at rates faster than most models project 7, indicating that there are processes contributing to Arctic amplification that are not being fully captured in the current generation of global climate models.
The feedbacks underlying Arctic amplification include the ice-albedo feedback, lapse rate, and Plank feedbacks, which lead to less efficient re-radiation to space at high-latitudes, as well as cloud feedbacks 8– 11. However, the role of the subpolar extratropics in modulating these feedbacks is not well-understood. Observed warming of the mid-troposphere in the Arctic indicates a role for enhanced atmospheric meridional heat transport in recent Arctic warming 1, 12, 13, most likely linked to increasing tropical and extratropical ocean temperatures. However, it is unclear which ocean regions may dominate this enhanced heat transport, and therefore exert the greatest influence on Arctic temperature and sea-ice feedbacks.
Decadal-scale modes of variability such as the Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation have been implicated in Arctic sea-ice variability 2, 14, 15, indicating patterns of ocean-atmospheric variability may affect Arctic climate differently. Record-breaking Arctic warming and sea-ice decline has ensued in recent years 16, paralleling anomalous ocean warming in the Northeast Pacific 17 (Fig. 1a, c). Furthermore, sea surface temperature (SST) pattern formation is predicted to be non-stationary in response to changes in radiative forcing, with the most pronounced increase in surface ocean temperatures projected to occur in the North Pacific in the coming decades to centuries (Fig. 1b); an effect attributed to the shallow mixed layer depth that allows fast equilibration with the atmosphere 18, 19. We refer to this amplified SST response in the North Pacific on decadal to centennial time scales in response to radiative forcing as the ‘North Pacific Rapid Response’ (as distinguished from slower responses that might occur after adjustment of large ice sheets and deep ocean properties). In contrast to the North Pacific Rapid Response, SSTs in the subpolar North Atlantic are predicted to remain lower than global averages in the coming century due to deeper convection and mixing with cool subsurface waters (Fig. 1b, e.g., ref. 20).
Recent and projected surface air temperatures in context of past abrupt warming events in the northern hemisphere. Recent annual surface air temperature (SAT) anomalies for 2014–2016 relative to 1890–1970 ( a; refs. 58, 59), CMIP5 projected SAT anomalies by the end of the century in RCP 8.5 ( b), and paleoclimate records spanning the past 19,000 years that document abrupt Arctic warming in concert with abrupt warming in the mid-latitude North Pacific and North Atlantic basins ( c). Colored dots on map in b represent the locations for the paleoclimate datasets plotted in c. Record-breaking Arctic warming in recent years was associated with anomalously high SSTs in the Northeast Pacific despite a cold spot in the North Atlantic ( a). Projected SATs show an amplified response in the Arctic and North Pacific, whereas North Atlantic SSTs undergo relatively modest warming in the first century ( b). c Paleoclimate archives spanning the last 19,050 years (1950 = year 0). From top: Greenland δ 18O records from NEEM 19,000–10,000 years ago (dark blue 26) and NGRIP (light blue 60), An alkenone-based UK’37 SST reconstruction from the Gulf of Alaska reflecting Summer SST (dark green 22); the youngest data point represents the core top measurement taken in 2004 (ref. 61); September SST data from Ocean Station Papa (145°W, 50°N) from 2007 to 2015 are plotted on the same axis (light green squares; http://www.pmel.noaa.gov/ocs/data). An SST reconstruction based on faunal assemblages from the central North Atlantic (plum 24), and a reconstruction of the average North Atlantic SST anomaly relative to Holocene values (pink 25). A composite record of the global temperature anomaly for the past 20,000 years relative to the 1980–2004 mean (blue 25, 62, 63). Atmospheric CO 2 concentrations from 19,000 years ago to present based on ice cores measurement and modern measurements 64. Gray shaded bars represent two episodes of rapid warming in Greenland that are coeval (within age uncertainties) with abrupt SST increase in the Northeast Pacific and North Atlantic, increases in CO 2 concentration, and intervals of widespread hypoxia in the North Pacific 22, 28 and Holarctic megafauna extinctions 29. Pink bar denotes changes since 1950
Paleoclimate records reveal episodes of abrupt ocean warming in the mid-latitude North Pacific 21– 23 and North Atlantic 24, 25 during the last deglaciation that were coeval with exceptionally rapid warming events of ~15°C in Greenland that occurred within decades to centuries 26 (Fig. 1c). These warming events were also accompanied by enhanced meridional flow of Pacific moisture transport across Alaska 27, implying a strong coupling of extratropical northern hemisphere SST changes with major reorganizations of poleward moisture transport and rapid Arctic climate change. These past ocean warming events had similar rates of SST increase to those predicted for the coming century under the RCP 8.5 scenario (~2–5°C/100 yr) 4, 22, and were associated with the crossing of major ecological thresholds across the northern hemisphere, including widespread North Pacific ocean hypoxia 22, 28 and Holarctic megafauna extinctions 29.
Given the observed coupling between abrupt Arctic temperature fluctuations and rapid SST changes in both the North Atlantic and North Pacific in the past, and the projected asymmetries in SST patterns in the near future, it is important to disentangle the relative impacts of changes in ocean heat flux sourced from each basin on Arctic temperature and sea ice extent to identify the teleconnections to which the Arctic may be most sensitive. Understanding such remote controls on Arctic climate will not only help to identify mechanisms that may lead to non-linear Arctic climate response in relation to changing SST patterns, but may help to reveal the primary sources of model bias that lead to underestimation of Arctic warming and sea-ice decline.
We imposed a series of ocean-to-atmosphere heat flux perturbations to the North Pacific and North Atlantic (30–60°N) in a slab-ocean configuration of the National Center for Atmospheric Research’s Community Earth System Model (CESM) version 1.0.4 30 (Supplementary Figure 1, Table 1) 31 to assess the relative climatic impacts of changes in ocean heat flux from each basin on Arctic surface temperature and climate feedbacks. We impose both positive (into the atmosphere) (NP-Warm, NA-Warm) and negative (into the ocean) heat fluxes in each basin (NP-Cool, NA-Cool), aggregating to a global average of ±1 W/m 2. We also impose heat fluxes in both basins to compensate each other, such that the only change is in effective zonal heat transport and the net global energy perturbation is zero (NP-Cool/NA-Warm, NP-Warm/NA-Cool, i.e., the seesaw simulations).
Summary of simulations conducted in this study
| Simulation name | Q-flux modification (global-equivalent W/m 2) | Topography modification | Global surface temperature (K) | Δ Global surface temperature (K) | Δ Arctic surface temperature (K) | Δ Arctic sea-ice fraction (%) | Δ Arctic low-cloud fraction (%) | Δ Arctic moisture convergence (%) | Arctic amplification factor | Climate feedback parameter α (W/m 2K) |
|---|---|---|---|---|---|---|---|---|---|---|
| Preindustrial Control | — | — | 286.56 ± 0.02 | — | — | — | — | — | — | — |
| NP-Warm | North Pacific +1 | — | 287.51 ± 0.02 | 0.95 ± 0.03 | 3.08 ± 0.20 | −7.36 ± 0.54 | 12.4 ± 0.89 | 18.23 ± 2.47 | 3.25 | 1.05 |
| NEP-Warm | Northeast Pacific +1 | — | 287.47 ± 0.05 | 0.91 ± 0.24 | 2.55 ± 0.68 | −5.81 ± 1.51 | 9.99 ± 2.70 | 17.1 ± 6.25 | 2.80 | 1.10 |
| NA-Warm | North Atlantic +1 | — | 287.35 ± 0.02 | 0.79 ± 0.02 | 2.12 ± 0.20 | −4.98 ± 0.56 | 9.5 ± 0.87 | 15.14 ± 2.53 | 2.68 | 1.27 |
| NP-Warm/NA-Cool | North Pacific +1 North Atlantic −1 | — | 286.53 ± 0.02 | −0.030 ± 0.03 | 0.34 ± 0.22 | −0.31 ± 0.56 | −0.33 ± 0.93 | −0.05 ± 2.26 | — | — |
| NP-Cool/NA-Warm | North Pacific −1 North Atlantic +1 | — | 286.18 ± 0.03 | −0.39 ± 0.03 | −1.88 ± 0.14 | 4.07 ± 0.21 | 4.80 ± 0.29 | −4.84 ± 2.28 | — | — |
| NP-Cool | North Pacific −1 | — | 284.93 ± 0.02 | −1.63 ± 0.02 | −5.56 ± 0.15 | 9.96 ± 0.21 | −17.35 ± 0.29 | −24.69 ± 1.57 | 3.41 | 0.61 |
| NA-Cool | North Atlantic −1 | — | 285.45 ± 0.02 | −1.11 ± 0.02 | −3.29 ± 0.14 | 6.18 ± 0.22 | −11.65 ± 0.29 | −18.03 ± 1.62 | 2.96 | 0.90 |
| Control Notopo | — | Global land topography = 0 | 286.94 | — | — | — | — | — | — | — |
| NP-Warm-Notopo | North Pacific +1 | Global land topography = 0 | 287.97 | 1.03 | 3.23 | −7.33 | — | 18.60 | 3.14 | 0.97 |
| NA-Warm-Notopo | North Atlantic +1 | Global land topography = 0 | 287.81 | 0.87 | 2.43 | −4.90 | — | 16.36 | 2.79 | 1.15 |
Reported errors are the 95% confidence interval calculated as the standard error multiplied by 1.96, accounting for autocorrelation in time series (see Methods)


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4.14 Types of reduplication.
In Chapter 3 we discussed Initial Reduplication (rule P-2) and Final Reduplication (P-3). Only P-2 is relevant to nouns. As explained in §3.3, the basic generalisation is that monosyllables and stems beginning with a stop have monosyllabic reduplication /C1V1-/ while polysyllabic stems beginning with something other than a stop have a bisyllabic reduplication /C1V1C2V2/ Further complications are discussed in that section.
For nouns, the regular grammatical function of reduplication by P-2 is (3+) plural marking. This function is found with a large number of adjectival nouns (NAdj), and hence with translation equivalents of many English human nouns (other than kin terms and Personal names). However, many such nouns take a Pl prefix such as /mij-/ instead of reduplicating, so the Pl must normally be specified in dictionary entries. Reduplicative NAdj may be used for nonhuman Pl reference. However, (nonadjectival) nonhuman nouns themselves have no plural marking (except for collectives, see below).
Some exx. of regular Pl reduplication by P-2 for NAdj with plural (more than three) sense are these:
| Sg | Pl | gloss |
| badirinya | ba-badirinya | ghost |
| dhudabada | dhu-dhudabada | white man |
| lhalmar | lhalma-lhalmar | foreigner |
| ngajiwanggu | ngaja-ngajiwanggu | old woman |
| (In nonpredicative use, the Sg forms shown would be modified by adding the relevant HumSg suffix, except that this is only optional for ’ghost'.) |
There are also some high-frequency irregular plurals which in some cases look like irregular reduplications:
| Sg | Pl | gloss |
| manung | mananung | woman |
| w2irig | w2ura:yung | small |
| w2inyig | w2unya:nyung | small |
(The same remark about use of HumSg suffix made above applies to these items as well.) The two Pl forms for 'small' are also unusual in that, with human reference, Pl prefix /mij-/ is added, hence /mij-bura:yung/ and /mij-bunya:nyung/ 'children'.
The two stems for 'small' consistently use the irregular Pl form for plural sense with nonhuman as well as human nouns. Since nonhuman nouns (except for a handful of collectives, see below) do not mark plural, either in their own affixes or in cross-referencing pronominals in the verb, the Pl form of a modifying adjective like 'small' may be the only evidence of (cryptotypic) plurality of such nouns. For example, /mada/ 'grass' has no direct Pl form, and since the English counterpart is usually a mass noun we might assume this is also true of the Nunggubuyu word. However, /mada/ may co-occur with a NAdj like 'small', and in this case we may get /w2unya:nyung/ suggesting a (usually hidden) underlying plurality.
Some other NAdj with similar high-frequency Pl forms used with nonhuman as well as human referent are these:
| Sg | Pl | gloss |
| lhamungur | lhama-lhamungur | short |
| ala:di | ala:-'la:di | bad |
| runggal | runggu-runggal | big |
On the other hand, for many of the less common NAdj which take a reduplicative Pl, the reduplication is sometimes or even frequently omitted. (For //lh// → /dh/ in 'short', see P-19.)
In addition to the Pl reduplication just illustrated, we have at least two other types of nominal reduplication using essentially the same phonological rule (P-2): collective and adjectival. The term 'collective' is applied to a few topographic nouns which reduplicate to indicate a substantial but indefinite number of instances:
| Sg | Collective | gloss |
| ala | ala:-'la | river |
| wurugu | wuru-wurugu | billabong |
| rilji | rilji-rilji | island |
The adjectival type involves reduplication along with a suffix /-j/ which may ablaut the preceding vowel to /u/ The reduplication is obligatory in most or all of the stems I have in mind, but there is often a cognate verb which may fluctuate between simple and reduplicated form. In any event, the reduplication is not specifically plural or collective in the noun forms (and need not be specifically repetitive or prolonged in the verbs). See §14.15.
| Verb | NAdj | ||
| =w2ara- | 'be crooked' | w2ara-waru-j | 'crooked' (cf. P-19) |
| =w2ilwila- | 'be pliant' | w2il-wilwilu-j | 'pliant' ( " " ) |
| =duma- | 'be black' | du-duma-j | 'black' |
| =ngalngala- | 'be white' | ngal-ngalngalu-j | 'white' |
There are also a number of frozen formations of this type for which a synchronic segmentation is questionable, as with /jarmayarmaj/ 'long, tall'. There are also some cases where a noun or NAdj is derived from a simple noun root by reduplication and addition of /-yij/ as in /mada/ 'grass', derivative /mada-mada-yij/ 'having lots of grass'. For more details and exx. see §14.14. It should be noted that in the exx. 'crooked' and 'pliant' just given, the //w2// becomes /b/ by Leftward-Hardening P-19 in some contexts, just as //lh// in 'short' (above, this page) becomes /dh/.
In addition to plural, collective, and adjectival reduplications, there are a few other possibly distinct minor types. The time-of-day adverbial nouns /ngamugijgaj/ 'morning' and /adharwara/ 'dusk' are often found in reduplicated form with no specifically repetitive or plural sense: /ngama-ngamugijgaj/ /adha:-'dharwara/ This may be related to the tendency of cardinal-direction adverbs to take special reduplicative forms (§7.29).
From /mu:n/ 'foot' a form /mu-mu(:)n-gala/ 'on foot' is recorded (NMET
Alongside adverbial noun /ragij/ 'first (in place)' we have a more or less equivalent form /raga-ragi-j/ which has no specific Pl meaning. Perhaps this is related to the isolated reduplications of other adverbial forms just mentioned.
From /rangag/ 'tree' (ANAwu class) or 'dugout canoe' (MANA), we have /ranga-rangag/ 'wooden seats in canoe' (ANAwu). Here the reduplication has an idiosyncratic derivational function. The various nominal reduplications we have seen occur at the beginning of the root or stem, and do not include the NCinfl prefix. Even with monosyllabic roots, suffixes such as case markers cannot be copied in the reduplicative segment. NCder prefixes are part of the stem and thus are affected by reduplication, so that //Rdp-uG-muwaj// shows up as //uGmu-uG-muwaj// → /(w)umu:-'-muwaj/ 'named (human Pl)', with //-uG-// as Pl NCder prefix.


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A Cell stores the data for one cell in the DataTable.
Various cells are grouped together in a Row.
Please visit Google's API documentation at https://google-developers.appspot.com/chart/interactive/docs/reference#cell_object for detailed background information.
Located in /googlecharttools/model/Cell.class.php (line 42)
Creates a new Cell.
Gets the cell's formatted value.
Gets the cell's properties.
Gets the cell's value.
Sets the cell's formatted value.
If not set, the cell's value will be displayed.
Sets the cell's properties.
Sets the cell's value.
Converts this object into a JSON-object.
This representation follows the JavaScript literal format specified at https://google-developers.appspot.com/chart/interactive/docs/reference#dataparam as an element of the "row" JSON-array.
Documentation generated on Mon, 01 Oct 2012 16:45:02 +0200 by phpDocumentor 1.4.4


Consumer that dumps messages out to standard out.
Performance test for the full zookeeper consumer
A utility that retrieve the offset of broker partitions in ZK and prints to an output file in the following format:
A utility that updates the offset of broker partitions in ZK.
The mirror maker has the following architecture: - There are N mirror maker thread shares one ZookeeperConsumerConnector and each owns a Kafka stream.
Load test for the producer
For verifying the consistency among replicas.
Performance test for the simple consumer
Command line program to dump out messages to standard out using the simple consumer
A utility that merges the state change logs (possibly obtained from different brokers and over multiple days).
This is a torture test that runs against an existing broker.
A utility that updates the offset of every broker partition to the offset of earliest or latest log segment file, in ZK.
ZooKeeper 3.


This site is dedicated to the best boy in the entire world
Theodore is a labrador-retriever mix from a local shelter. He came to us at 6mos old.  His family loves him very much, and despite not always wanting to accept our hugs and affection, we know he loves us, too.


Namespace chia.dotnet
Classes
ColouredCoinWallet
Wraps a Coloured Coin wallet
Config
Represents a chia config yaml file and its contents. Used to find the uri and ssl certs needed to connect via a WebSocket
Daemon
RpcClient for the daemon interface. The daemon can be used to proxy messages to and from other chia services.
DIDWallet
Wraps a Distributed Identity Wallet
EndpointInfo
Information about how to connect and authenticate with the RPC endpoint
FarmerProxy
Proxy that communicates with the farmer via the daemon
FullNodeProxy
Proxy that communicates with the full node via the daemon
HarvesterProxy
Proxy that communicates with the harvester via the daemon
Message
The messaging data structure for request and response exchange with the RPC endpoint
PlotterConfig
Configuration settings for the plotter. (equivalent to chia plots create command line args)
PlotterProxy
Class to manage plotting
PoolWallet
Wraps a Pool Wallet
RateLimitedWallet
Wraps a Coloured Coin wallet
ResponseException
Exception thrown when the RPC endpoint returns a response Message but Data.success is false
RpcClient
Base class that handles core websocket communication with the rpc endpoint and synchronizes request and response messages
ServiceProxy
Base class that uses a Daemon to send and receive messages to other services
Wallet
Base class representing a specific wallet (i.e. anything with a WalletID)
WalletProxy
Proxy that communicates with the wallet via the daemon
Structs
ServiceNames
The names of chia services. These are used as Destination values
Enums
KValues
Valid plot sizes


{% if reply %} Koanbot says: {{reply}} {% endif %} Click here to see how this sentence was generated {% if sausage %} {% for key, value in sausage.items() %} {{value|safe}} {% endfor %} {% endif %}


I would like to share my experience, the time I got my back teeth pulled at the dentist. Previous to this, I probably have inhaled nitrous about 15 times, mostly in whippet form.
I arrive at the dentist. It is pretty busy, after waiting about 25 minutes they put me in the dental surgery room. They put the mask on, which they call the 'snoopy nose', because the dentist's office is mainly for kids.
I inhale the sweet gas at a normal breathing rate. I sit patiently, nervous about having my teeth yanked out. I start to get staticy vision, and a buzzing sound vibrated in my head. Then, the wood grain wall started bending and swirling around in a spiral. I am enjoying this, my body is comfortably numb. I feel like me and the chair are starting to get sucked into the spiraling wood.
My consciousness started to become dissociated, I guess around 10 minutes. I wasn't thinking clearly, and I began to wonder where I was. I looked up at the ceiling. In the room there are shapes hanging from the ceiling, circles, squares and triangles. I looked at these and thought I was in math class for some reason.
Then came the dentist. He injected my gums with novocaine. I didn't really know what was going on. I started biting my lip. The assistant dentist said 'hey, quit putting holes in your lip.' I didn't know, but blood was running down my chin and they were putting cloth on it.
Then, I have an out of body hallucination. The dentist has a crazy smile, like on Batman the movie when people had those smiles like the joker. I was watching from about 5 feet away as the dentist started to spin my chair around really fast. The assistants also had the smile.
I screamed 'I'M IN HELL!' spitting out the gauze, blood spilling out of my mouth. The teeth were pulled, and I didn't realize it happened. An assistant ran over. 'Are you ok??' I heard them say 'We need to give him oxygen now'.
I slowly started to come out of the weird daze. They helped me to the waiting room and told me to wait at least a half hour before driving. I sat there, still buzzing. My mouth and lips were so sore, I was holding gauze to my face. It was also in my mouth. I was still bleeding, but they gave me plenty of cloth. I drove home after I felt sober, and got home and took some B vitamins.
This is a really unexpected result of nitrous, I think. I never had this kind of trip on whippets. This was a bad trip, but some of it was fun and interesting.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


-   Prise:  $30
-   In Stock:  by request
Stepper Bipolar HC
Product Features
This tiny bipolar stepper has a 1.8° step angle and 600 g·cm of holding torque.
Product Description
This is a nice, small, efficient stepper motor with good torque characteristics.
-   Prise:  $30
-   In Stock:  by request
NEMA-17 Bipolar 48mm Stepper (0.9° Step Angle)
Product Features
This precise bipolar stepper has a 0.9° step angle and 4.28 kg·cm of torque at low speeds.
Product Description
This motor is a larger NEMA-17 motor that produces a whopping 4800g-cm of holding torque and fine resolution. The motor is also very efficient in converting current into torque and velocity.
-   Prise:  $30
-   In Stock:  by request
NEMA-17 Bipolar 32mm Stepper
Product Features
This high speed bipolar stepper has a 3.75° step angle and 590 g·cm of torque at low speeds.
Product Description
This is our fastest Stepper Motor. It provides good torque at slightly less resolution than our other steppers.
-   Prise:  $30
-   In Stock:  by request
NEMA-17 Bipolar 20mm Stepper
Product Features
This compact bipolar stepper has a 1.8° step angle and 450 g·cm of torque at low speeds.
Product Description
This motor is a NEMA-17 motor that is only 20mm long. Great for projects where space is restricted and that do not require too much torque.
-   Prise:  $30
-   In Stock:  by request
NEMA-23 Bipolar 76mm Stepper
Product Features
This powerful bipolar stepper has a 1.8° step angle and 11.06 kg·cm of torque at low speeds.
Product Description
At 12.5 Kg-cm of holding torque, this is a powerful gearless stepper motor.
-   Prise:  $13
-   In Stock:  by request
950g-cm Unipolar Stepper
Product Features
This affordable unipolar stepper has a 7.5° step angle and 710 g·cm of torque at low speeds.
Product Description
This is the motor you want if you are looking for a low cost unipolar stepper that needs to do some work.
-   Prise:  $10
-   In Stock:  by request
140g-cm Unipolar Stepper
Product Features
This affordable unipolar stepper has a 7.5° step angle and 710 g·cm of torque at low speeds.
Product Description
This is the motor you want if you are looking for a low cost unipolar stepper that needs to do some work.
-   Prise:  $11
-   In Stock:  by request
50g-cm Unipolar Stepper
Product Features
This high speed unipolar stepper has an 18° step angle and 30 g·cm of torque at low speeds.
Product Description
This motor is good for turning small knobs, buttons, etc.
-   Prise:  $90
-   In Stock:  by request
NEMA-17 Bipolar 99.51:1 Planetary Gearbox Stepper
Product Features
This bipolar stepper with planetary gearbox has a 0.018° step angle and 48 kg·cm of torque at low speeds.
Product Description
This NEMA-17 motor has an integrated Planetary gearbox with a 991044/2057 :1 ratio. At 1.6 Amps (maximum current), this gearbox stepper can produce a maximum torque of 250 kg-cm. However, the gearbox is only rated for 48 kg-cm of continuous torque, and 100 kg-cm for brief overloads. Loading this gearbox stepper beyond the torque rating of the gearbox will shorten its useful life.
At the output of the gearbox, the step angle is approximately 0.018°. When using the step angle in calculations, you should derive the exact step angle by dividing 1.8° by the gearbox reduction ratio.
This particular revision of the motor is made by a different manufacturer, but should behave very similarly to the revision 0 version.
-   Prise:  $32
-   In Stock:  by request
28STH32 NEMA-11 Bipolar Gearless Stepper Motor
Product Features
This tiny stepper with a rear shaft has a 1.8° step angle and 520 g·cm of torque at low speeds.
Product Description
This NEMA-11 motor generates 600 g-cm of holding torque at 0.67 Amps. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $72
-   In Stock:  by request
28STH32 NEMA-11 Bipolar Stepper with 27:1 Gearbox
Product Features
This compact bipolar stepper with a rear shaft and a planetary gearbox has a 0.067° step angle and 14 kg·cm of torque at low speeds.
Product Description
This NEMA-11 motor has an integrated Planetary gearbox with a 26103/121 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $76
-   In Stock:  by request
28STH32 NEMA-11 Bipolar Stepper with 100:1 Gearbox
Product Features
This compact bipolar stepper with a rear shaft and a planetary gearbox has a 0.018° step angle and 32 kg·cm of torque at low speeds.
Product Description
This NEMA-11 motor has a Planetary gearbox with a 991044/2057 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $32
-   In Stock:  by request
35STH36 NEMA 14 Bipolar Gearless Stepper
Product Features
This bipolar stepper with rear shaft has a 1.8° step angle and 1.2 kg·cm of torque at low speeds.
Product Description
This NEMA-14 motor generates 1.4 Kg-cm of holding torque at 1 Amp. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $32
-   In Stock:  by request
35STH36 NEMA 14 Bipolar Gearless Stepper
Product Features
This affordable bipolar stepper with a rear shaft has a 1.8° step angle and 3.3 kg·cm of torque at low speeds.
Product Description
This NEMA-17 motor generates 3.6 Kg-cm of holding torque at 1.7 Amps. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $80
-   In Stock:  by request
42STH38 NEMA-17 Bipolar Stepper with 5.18:1 Gearbox
Product Features
This bipolar stepper with a rear shaft and a planetary gearbox has a 0.35° step angle and 18 kg·cm of torque at low speeds.
Product Description
This NEMA-17 motor has an integrated Planetary gearbox with a 52/11 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $84
-   In Stock:  by request
42STH38 NEMA-17 Bipolar Stepper with 14:1 Gearbox
Product Features
This bipolar stepper with a rear shaft and a planetary gearbox has a 0.13° step angle and 30 kg·cm of torque at low speeds.
Product Description
This NEMA-17 motor has an integrated Planetary gearbox with a 13212/289 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $88
-   In Stock:  by request
42STH38 NEMA-17 Bipolar Stepper with 26.85:1 Gearbox
Product Features
This bipolar stepper with gearbox and rear shaft has a 0.067° step angle and 30 kg·cm of torque at low speeds.
Product Description
This NEMA-17 motor has an integrated Planetary gearbox with a 26 103/121 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
At 1.7 Amps, this stepper motor can produce a maximum torque of 77 kg-cm. However, the gearbox is only rated for 30 kg-cm of continuous torque, and 80 kg-cm for brief overloads. Loading this gearbox stepper beyond the torque rating of the gearbox will shorten its useful life.
-   Prise:  $92
-   In Stock:  by request
42STH38 NEMA-17 Bipolar Stepper with 51:1 Gearbox
Product Features
This bipolar stepper with a rear shaft and a planetary gearbox has a 0.035° step angle and 48 kg·cm of torque at low speeds.
Product Description
This NEMA-17 motor has an integrated Planetary gearbox with a 50 4397/4913 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $96
-   In Stock:  by request
42STH38 NEMA-17 Bipolar Stepper with 99.51:1 Gearbox
Product Features
This bipolar stepper with a rear shaft and a planetary gearbox has a 0.018° step angle and 48 kg·cm of torque at low speeds.
Product Description
This NEMA-17 motor has an integrated Planetary gearbox with a 991044/2057 :1 ratio.It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $56
-   In Stock:  by request
57STH56 NEMA 23 Bipolar Precision Gearless Stepper
Product Features
This precision bipolar stepper with rear shaft has a 0.9° step angle and 11.2 kg·cm of torque at low speeds.
Product Description
This NEMA-23 motor generates 12 kg-cm of holding torque at 2.8 Amps. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
This motor is particulary suited for precision applications due to its 0.9° step angle.
-   Prise:  $40
-   In Stock:  by request
57STH56 NEMA-23 Bipolar Gearless Stepper
Product Features
This high-speed bipolar stepper with a rear shaft has a 1.8° step angle and 11 kg·cm of torque at low speeds.
Product Description
This NEMA-23 motor generates 12.6 kg-cm of holding torque at 2.8 Amps. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $40
-   In Stock:  by request
57STH56 NEMA-23 Bipolar Stepper with 4.25:1 Gearbox
Product Features
This bipolar stepper with a rear shaft and a planetary gearbox has a 0.42° step angle and 46.6 kg·cm of torque at low speeds.
Product Description
This NEMA-23 motor has an integrated Planetary gearbox with a 41/4 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $144
-   In Stock:  by request
57STH56 NEMA-23 Bipolar Stepper with 15:1 Gearbox
Product Features
This powerful bipolar stepper with a rear shaft and a planetary gearbox has a 0.12° step angle and 150 kg·cm of torque at low speeds.
Product Description
This NEMA-23 motor has an integrated Planetary gearbox with a
15 3/10 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $148
-   In Stock:  by request
57STH56 NEMA-23 Bipolar Stepper with 77:1 Gearbox
Product Features
This powerful bipolar stepper with a rear shaft and a planetary gearbox has a 0.023° step angle and 240 kg·cm of torque at low speeds.
Product Description
This NEMA-23 motor has an integrated Planetary gearbox with a
76 49/64 :1 ratio. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $144
-   In Stock:  by request
57STH56 NEMA-23 Bipolar Stepper with 15:1 Gearbox
Product Features
This huge bipolar stepper with a rear shaft has a 1.8° step angle and 30 kg·cm of torque at low speeds.
Product Description
This NEMA-34 motor generates 34 kg-cm of holding torque at 2.8 Amps. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
-   Prise:  $160
-   In Stock:  by request
86STH156 NEMA-34 Bipolar Gearless Large Stepper
Product Features
This massive bipolar stepper with a rear shaft has a 1.8° step angle and over 100 kg·cm of torque at low speeds.
Product Description
This NEMA-11 motor generates 122 kg-cm of holding torque at 4.2 Amps. It comes with the rear shaft exposed, so you can mount an encoder or shaft coupler.
We Provide:
- To be accessible, friendly, and helpful at all times.
- To answer the phone when you call, 24/7/365.
- Reply to your emails and support tickets within 24 hours.
- To provide timely information about new hardware.
- To provide all design documentations.
Please fulfill following request form to get more information!


abstract fun getChildCollection(): List<ChildItem>
Supply the list of ChildItem under this ParentItem
Return List of ChileItem under this ParentItem
See Also
ChildItem


Wed Feb 23, 2000
On the train… the next morning.
Well, I was the only one having the three-course fiasco last night, so it was kind of silly. And it looks like I’m the only one having the breakfast this morning! Geez, I could really use a shower man. And my hair washed.
It’s raining outside and I feel so lazy. Thinking about just catching a taxi to the hostel. Shouldn’t be more than $50? Have to book my bus back to Jhb today - oops, I forgot about the one back to Durbs… oh oh.
Just had breakfast with this really nice guy from Bulawayo. The Italian guy also pitched up - he’s a bit annoying though.
Just listening to the news: big floods in Chimanimani and Beitbridge! Hope I can make it back, sounds serious - Cyclone Eline causing it all.
Well, the taxi cost me $71, but it was worth it, cause it’s pissing down.
I think I’ll have to brave the rain after lunch. It’s kind of cold but if I wear my slops and my swimming trunks, at least they dry quickly.
Couldn’t get through to Colleen today - it’s raining so the phones don’t work. Hostel’s getting kind of full. Hope none of these guys snore!
Tired.
I had my second really scary experience today - one of those “Hello my friend” guys came up and I ignored him as I do sometimes and this guy grabbed my arm and wouldn’t let go and was getting really pissed.
Didn’t know it was this bad, but did you know one in two Zimbabwean adults is HIV positive?
This journal entry has been transcribed from the handwritten original. For more information about this transcription project, see this blog post.


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Debug Board
The debug board displays the state of the bus. This includes two 7-segment LED digits for the data pins, four digits for the address pins, and ten individual LEDs: four IRQs (interrupt request pins), memory read, memory write, I/O read, I/O write, reset, and NMI (non-maskable interrupt).
The design of the debug board is fairly straightforward. The individual LEDs are hooked up to the bus through inverters (since these lines are active-low) and through resistor packs. The data and address pins go through 4-bit-to-7-segment-LED chips (4495s).
We used the debug board extensively when building the memory and CPU boards. We didn't use it after that because we couldn't run the I/O board any slower than full speed and that prevented us from doing single-stepping of the Z-80 code.


Carl Jae Kyung Dins
Our son was born on February 10th, 2017 at 3:55 PM local time. His passport will read “Carl Jae Kyung Dins”, and his parents will call him “Jae Kyung”. He is the sixth Carl Dins and the first to be born outside the USA since Carl F. W. Dins (born in 1834) emigrated from Prussia. Using the Korean alphabet, this is “재경” and in a Korean context requiring a full name, he may use his mother’s family name — “김재경” (Kim Jae Kyung).
With Chinese characters (called “hanja” in Korean) being the predominate form of writing in Korea until the 1900s, some words and names can still be represented with hanja. After selecting Jae Kyung because we liked the sound, we selected hanja “在經” to add a level of meaning. (Adding his mother’s family name, this would be “金在經”). In both modern Korean and Chinese, these characters have similar meanings and usages: 在 is a preposition “at” or “in”, and 經 is used in the words “scripture” and “Bible”. 在 and 經 are used in other words and contexts, so a Korean or Chinese speaker would not immediately associate “in the Word” upon seeing 在經, but it would be clear with a little explanation of his parents’ intention.


Package com.ae2dms.UI.Game
Class GameViewController
java.lang.Object
com.ae2dms.UI.AbstractBarController
com.ae2dms.UI.Game.GameViewController
public class GameViewController extends AbstractBarController
The Controller of GameView in MVC pattern.
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Field Summary
Fields Modifier and Type Field Description private javafx.scene.control.Label
All_Level_Count
Text for Binding: count for all levels, on the topprivate javafx.scene.image.ImageView
Background_Image
The Background Image of this game, will dynamically change between levelsprivate javafx.scene.Group
Can_Blur_Group
Content Group, used for blurring when pop up showsprivate javafx.scene.layout.GridPane
candyGrid
The JavaFX GridPane as candyGrid to add Candy, assigned in constructorprivate javafx.beans.property.BooleanProperty
canUndo
whether can undo at this status bind to view to determine if the undo button can click bind to GameStageSaver.canUndoprivate javafx.scene.layout.GridPane
diamondsGrid
The JavaFX GridPane as diamondsGrid to add Diamond, assigned in constructorprivate javafx.scene.layout.Pane
DraggableFloor
JavaFX Pane for DraggableFloorprivate DraggableFloorController
DraggableFloorController
Controller for DraggableFloorprotected javafx.scene.layout.BorderPane
ExitPopUp
JavaFX BorderPane for ExitPopUpprotected ExitPopUpController
ExitPopUpController
Controller for ExitPopUpprotected javafx.scene.layout.BorderPane
GameCompletePopUp
JavaFX BorderPane for GameCompletePopUpprotected GameCompletePopUpController
GameCompletePopUpController
Controller for GameCompletePopUpprivate GameDocument
gameDocument
GameDocument Instanceprivate GameStatus
gameStatus
The current game statusstatic javafx.beans.property.BooleanProperty
isAnimating
whether at this time the animation is happening or not will ignore all key events if still animatingprivate javafx.beans.property.BooleanProperty
isBlur
whether at this time the gameView is blurred or not will ignore all key events if still animatingstatic javafx.beans.property.BooleanProperty
isCheating
whether is Cheating at this time (Crate is left up for moving)private javafx.scene.input.KeyCombination
keyCombCtrM
KeyCombination of Ctrl(Command) + M for Musicprivate javafx.scene.input.KeyCombination
keyCombCtrS
KeyCombination of Ctrl(Command) + S for Saveprivate javafx.scene.input.KeyCombination
keyCombCtrZ
KeyCombination of Ctrl(Command) + Z for undoprotected javafx.scene.layout.BorderPane
LevelCompletePopUp
JavaFX BorderPane for LevelCompletePopUpprotected LevelCompletePopUpController
LevelCompletePopUpController
Controller for LevelCompletePopUpprivate javafx.scene.layout.GridPane
objectsGrid
The JavaFX GridPane as objectsGrid to add Crate and Player, assigned in constructorprivate GameMediaPlayer
player
GameMediaPlayer instancestatic GraphicRender
render
GraphicRender to render mapsprivate javafx.scene.control.Label
Score
Text for Binding: Score of current game, on the top_rightstatic java.util.HashMap<java.lang.String,javafx.scene.media.AudioClip>
soundEffects
The hashmap to store all sound effects, can get the AudioClip by searching the name with (SFX Name, AudioClip) pairprivate javafx.scene.layout.GridPane
stageGrid
The JavaFX GridPane as stageGrid to add Wall and Floor, assigned in constructorprivate javafx.scene.control.Label
This_Level_Index
Text for Binding: index for this level, on the topprivate javafx.scene.control.Label
Time_Spend
Text for Binding: Time duration spend, on the top-rightprivate GameTimer
timer
The concurrent game timer to count timeFields inherited from class com.ae2dms.UI.AbstractBarController
bestRecord, ColourPreference, ColourPreferenceController, debugIsActive, HighScoreBar, HighScoreBarController, highScoreIsShown, SoundPreference, SoundPreferenceController
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Constructor Summary
Constructors Constructor Description GameViewController()
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Method Summary
Modifier and Type Method Description private void
checkIsGameComplete()
Check if this Game is complete if so, pop up the GameCompletePopUp with steps and time duration, with text field where user can type in names to save recordprivate void
checkIsLevelComplete()
Check if this level is complete if so, pop up the LevelCompletePopUp with steps and time durationprivate void
clickSaveGame()
click save game buttonprivate void
clickToMenu()
click back to menu button will pop up a alert where you can save your game with adviceprivate void
clickUndo()
click undo buttonprivate void
exitGaussianBlur()
exit the state where game view is in Gaussian Blurprivate void
gaussianBlur()
enter the state where game view is in Gaussian Blurvoid
handleKey(javafx.scene.input.KeyEvent event)
handle the key event in the game view like the WSAD, Up Down Left and Rightvoid
initialize()
Initialize the GameView and Controller set the background image to a random one bind the level index, level count, steps and time count with the one in GameDocument render the game map with the map in GameDocumentprivate void
switchToNextLevel()
Change to the next level, if no more level, then call checkIsGameComplete()Methods inherited from class com.ae2dms.UI.AbstractBarController
disableButton, enableButton, menuBarClickDebug, menuBarClickHighScoreList, menuBarClickMusic
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Field Details
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LevelCompletePopUp
protected javafx.scene.layout.BorderPane LevelCompletePopUpJavaFX BorderPane for LevelCompletePopUp -
LevelCompletePopUpController
Controller for LevelCompletePopUp- See Also:
LevelCompletePopUpController
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GameCompletePopUp
protected javafx.scene.layout.BorderPane GameCompletePopUpJavaFX BorderPane for GameCompletePopUp -
GameCompletePopUpController
Controller for GameCompletePopUp- See Also:
GameCompletePopUpController
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ExitPopUp
protected javafx.scene.layout.BorderPane ExitPopUpJavaFX BorderPane for ExitPopUp -
ExitPopUpController
Controller for ExitPopUp- See Also:
ExitPopUpController
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DraggableFloor
private javafx.scene.layout.Pane DraggableFloorJavaFX Pane for DraggableFloor -
DraggableFloorController
Controller for DraggableFloor- See Also:
DraggableFloorController
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Can_Blur_Group
private javafx.scene.Group Can_Blur_GroupContent Group, used for blurring when pop up shows -
Background_Image
private javafx.scene.image.ImageView Background_ImageThe Background Image of this game, will dynamically change between levels -
Score
private javafx.scene.control.Label ScoreText for Binding: Score of current game, on the top_right -
Time_Spend
private javafx.scene.control.Label Time_SpendText for Binding: Time duration spend, on the top-right -
This_Level_Index
private javafx.scene.control.Label This_Level_IndexText for Binding: index for this level, on the top -
All_Level_Count
private javafx.scene.control.Label All_Level_CountText for Binding: count for all levels, on the top -
stageGrid
private javafx.scene.layout.GridPane stageGridThe JavaFX GridPane as stageGrid to add Wall and Floor, assigned in constructor -
objectsGrid
private javafx.scene.layout.GridPane objectsGridThe JavaFX GridPane as objectsGrid to add Crate and Player, assigned in constructor -
diamondsGrid
private javafx.scene.layout.GridPane diamondsGridThe JavaFX GridPane as diamondsGrid to add Diamond, assigned in constructor -
candyGrid
private javafx.scene.layout.GridPane candyGridThe JavaFX GridPane as candyGrid to add Candy, assigned in constructor -
player
GameMediaPlayer instance -
gameDocument
GameDocument Instance -
render
GraphicRender to render maps -
gameStatus
The current game status- See Also:
GameStatus
-
timer
The concurrent game timer to count time -
soundEffects
public static java.util.HashMap<java.lang.String,javafx.scene.media.AudioClip> soundEffectsThe hashmap to store all sound effects, can get the AudioClip by searching the name with (SFX Name, AudioClip) pair -
canUndo
private final javafx.beans.property.BooleanProperty canUndowhether can undo at this status bind to view to determine if the undo button can click bind to GameStageSaver.canUndo -
isCheating
public static javafx.beans.property.BooleanProperty isCheatingwhether is Cheating at this time (Crate is left up for moving) -
keyCombCtrZ
private final javafx.scene.input.KeyCombination keyCombCtrZKeyCombination of Ctrl(Command) + Z for undo -
keyCombCtrM
private final javafx.scene.input.KeyCombination keyCombCtrMKeyCombination of Ctrl(Command) + M for Music -
keyCombCtrS
private final javafx.scene.input.KeyCombination keyCombCtrSKeyCombination of Ctrl(Command) + S for Save -
isAnimating
public static final transient javafx.beans.property.BooleanProperty isAnimatingwhether at this time the animation is happening or not will ignore all key events if still animating -
isBlur
private final javafx.beans.property.BooleanProperty isBlurwhether at this time the gameView is blurred or not will ignore all key events if still animating
-
-
Constructor Details
-
GameViewController
public GameViewController()
-
-
Method Details
-
initialize
public void initialize()Initialize the GameView and Controller set the background image to a random one bind the level index, level count, steps and time count with the one in GameDocument render the game map with the map in GameDocument -
handleKey
public void handleKey(javafx.scene.input.KeyEvent event)handle the key event in the game view like the WSAD, Up Down Left and Right- Parameters:
event
- keyboard event
-
checkIsLevelComplete
private void checkIsLevelComplete()Check if this level is complete if so, pop up the LevelCompletePopUp with steps and time duration -
checkIsGameComplete
private void checkIsGameComplete()Check if this Game is complete if so, pop up the GameCompletePopUp with steps and time duration, with text field where user can type in names to save record -
switchToNextLevel
private void switchToNextLevel()Change to the next level, if no more level, then call checkIsGameComplete() -
clickUndo
private void clickUndo()click undo button -
clickSaveGame
private void clickSaveGame()click save game button -
clickToMenu
private void clickToMenu()click back to menu button will pop up a alert where you can save your game with advice -
exitGaussianBlur
private void exitGaussianBlur()exit the state where game view is in Gaussian Blur -
gaussianBlur
private void gaussianBlur()enter the state where game view is in Gaussian Blur
-


Figure 1
Approximate cloud coverage
map based on corrected surface reflectance obtained by the VIIRS imager,
averaged over
the same date range and weighted by the weighted by
the visibility across TESS Sectors 1 and 2.
The dashed white lines indicate
latitudes
above/below which the visibility drops below 50%.


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| Class | Description |
|---|---|
| BinaryBlockInputFormat |
Custom binary block input format to return the custom record reader.
|
| BinaryBlockRecordReader |
Custom record reader for binary block.
|
| ByteBufferDataInput | |
| CollectionUtils | |
| CommonThreadPool |
This common thread pool provides an abstraction to obtain a shared
thread pool, specifically the ForkJoinPool.commonPool, for all requests
of the maximum degree of parallelism.
|
| DataConverter |
This class provides methods to read and write matrix blocks from to HDFS using different data formats.
|
| DMVUtils | |
| DnnUtils | |
| EMAUtils | |
| FastBufferedDataInputStream | |
| FastBufferedDataOutputStream |
This buffered output stream is essentially a merged version of
BufferedOutputStream and DataOutputStream, wrt SystemDS requirements.
|
| FastStringTokenizer |
This string tokenizer is essentially a simplified StringTokenizer.
|
| HDFSTool | |
| IndexRange | |
| InputStreamInputFormat |
Custom input format and record reader to redirect common implementation of csv read
over record readers (which are required for the parallel readers) to an input stream.
|
| LocalFileUtils | |
| LongLongDoubleHashMap |
This native long long - double hashmap is specifically designed for
ctable operations which only require addvalue - extract semantics.
|
| LongLongDoubleHashMap.ADoubleEntry | |
| NormalPRNGenerator |
Class that can generate a stream of random numbers from standard
normal distribution N(0,1).
|
| PoissonPRNGenerator |
Class that can generate a stream of random numbers from Poisson
distribution with specified mean.
|
| PRNGenerator | |
| ProgramConverter |
Program converter functionalities for
(1) creating deep copies of program blocks, instructions, function program blocks, and
(2) serializing and parsing of programs, program blocks, functions program blocks.
|
| Py4jConverterUtils |
Utils for converting python data to java.
|
| RandNPair |
Class that generates a pair of random numbers from standard normal
distribution N(0,1).
|
| SortUtils |
Utilities for sorting, primarily used for SparseRows.
|
| UniformPRNGenerator | |
| UtilFunctions |
| Enum | Description |
|---|---|
| DMVUtils.LEVEL_ENUM | |
| LongLongDoubleHashMap.EntryType |
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